Technical note: The effect of image annotation with minimal manual interaction for semiautomatic prostate segmentation in CT images using fully convolutional neural networks

Abstract

The goal is to study the performance improvement of a deep learning algorithm in three-dimensional (3D) image segmentation through incorporating minimal user interaction into a fully convolutional neural network (CNN). Methods: A U-Net CNN was trained and tested for 3D prostate segmentation in computed tomography (CT) images. To improve the segmentation accuracy, the CNN's input images were annotated with a set of border landmarks to supervise the network for segmenting the prostate. The network was trained and tested again with annotated images after 5, 10, 15, 20, or 30 landmark points were used. Results: Compared to fully automatic segmentation, the Dice similarity coefficient increased up to 9% when 5-30 sparse landmark points were involved, with the segmentation accuracy improving as more border landmarks were used. Conclusions: When a limited number of sparse border landmarks are used on the input image, the CNN performance approaches the interexpert observer difference observed in manual segmentation.

Empowering Vision Transformer by Optimal Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation

“…The original CT images of 512×512×N voxels had an intensity corresponding to the Hounsfield unit (HU), where N ranged from 61 to 375. During training in the transformer network, the input images were normalized to a range from 0 to 1 [41]. The network was trained using the AdamW optimizer and a modified loss function that combined cross entropy and dice loss under deep supervision.…”

Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation

“…The original CT images of 512 × 512 × N voxels had an intensity corresponding to the Hounsfield unit (HU), where N ranged from 61 to 375. During training in the transformer network, the input images were normalized to a range from 0 to 1[40]. The network was trained using the AdamW optimizer (an open-source Pytorch library) and a modified loss function that combined cross entropy and dice loss under deep supervision.…”